Pain and Cognitive Load As Predictors of Absenteeism in Industry

Abstract

Purpose: This study delves into the impact of pain and cognitive load on industrial absenteeism, aiming to highlight the importance of these factors in workforce management and productivity enhancement. Theoretical framework: Anchored in the biopsychosocial model, this research contends that absenteeism is intricately linked to the interplay between pain and cognitive load. Utilizing the Numeric Rating Scale (NRS) and the NASA Task Load Index (NASA-TLX), it seeks to elucidate their combined influence on absenteeism. Method/design/approach: In a pilot study, NRS and the NASA-TLX were utilized for data collection, with a third-party dataset incorporating seven predictor variables and absenteeism as the dependent variable. Statistical analysis involved the Shapiro-Wilk Test for data normality and the Mann-Whitney U Test for non-normal distributions. Generalized Linear Models were applied to analyze the impact of variables on absenteeism, including multivariate, univariate and combined models. Model performance was evaluated using BIC, AIC, pseudo-R², LogLik, and AUC, with the Delong method for AUC comparison. Results and conclusion: Pain emerged as a significant absenteeism predictor. The integrated model displayed enhanced discriminatory capacity, underscoring the pronounced interrelation between pain, cognitive load, and absenteeism. These insights advocate for holistic management strategies encompassing both health and occupational aspects. Research implications: This study reaffirms the crucial role of pain and cognitive load in industrial absenteeism and highlights the importance of a multifaceted, data-driven approach for effective intervention strategies. Originality/value: This study offers unique perspectives on absenteeism by merging pain and cognitive load assessments, presenting a comprehensive method for understanding and mitigating its impact on the industrial workforce.